• Land and Housing Review
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• v.2 no.4
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• pp.367-377
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• 2011

This paper investigates the determination factors' variation of real estate price after sub-prime financial crisis, in korea, using a VAR model. The model includes land price, housing price, housing rent (Jensei) price, which time period is from 2000:1Q to 2011:2Q and uses interest rate, real GDP, consumer price index, KOSPI, the number of housing construction, the amount of land sales and practices to impulse response and variance decomposition analysis. Data cover two sub-periods and divided by 2008:3Q that occurred the sub-prime crisis; one is a period of 2000:1Q to 2008:3Q, the other is based a period of 2000:1Q to 2011:2Q. As a result, Comparing sub-prime crisis before and after, land price come out that the influence of real GDP is expanding, but current interest rate's variation is weaken due to the stagnation of current economic status and housing construction market. Housing price is few influenced to interest rate and real GDP, but it is influenced its own variation or Jensei price's variation. According to the Jensei price's rapidly increasing in nowadays, housing price might be increasing a rising possibility. Jensei price is also weaken the influence of all economic index, housing price, comparing before sub-prime financial crisis and it is influenced its own variation the same housing price. As you know, real estate price is weakened market basic value factors such as, interest rate, real GDP, because it is influenced exogenous economic factors such as population structural changes. Economic participators, economic officials, consumer, construction supplyers need to access an accurate observation about current real estate market and economic status.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Journal of the Economic Geographical Society of Korea
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• v.22 no.4
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• pp.555-575
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• 2019

Though housing market varies spatially, housing policy is limited in reflecting detailed regional differentiation. This study looked at the differences in Seoul Gu Districts' response to housing policy for the efficient implementation of housing policies in the future. Housing policy index was established by each Gu-districts' according to investigated housing policies from 2003 to 2018, weighted in two categories(financial/urban planning) and the status of designated areas. The VECM model was established to analyze the impact of the housing policy on the housing market. According to the analysis, although housing policies were established in response to market prices change, the impact of policies on prices was lower than the impact of vice versa. The housing policy's impact to the housing market is limited in some areas in northeastern Seoul. These results show that there are differences in the responses to housing policy in Seoul districts', and therefore detailed consideration of the differences in the regional aspects of housing policy is needed.

• Journal of Digital Convergence
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• v.18 no.1
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• pp.121-127
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• 2020

This study analyzed through GEM-listed companies for verifying the interrelationship between positive and negative impacts on accounting information GEM-listed companies, whether venture firms operate the internal control operation system stably. Hong Kong's GEM listed company is a securities market similar to Korea's KOSDAQ market. To analyzing this study, used regression analysis method through internal control index to evaluate the operation of internal control system and discretionary accruals to evaluate the quality of accounting information. In this study, because profit adjustments used to realize through discretionary accruals, so analyzed using the modified Jones model to check whether the management deliberately transformed the company to realize future profits. In the empirical analysis, the correlation between the internal control index and the discretionary accruals to assess the quality of accounting information was able to find highly correlated. This study can provide useful guidance for evaluating the form and value of profit management of venture firms in the future, also would expect to help understand the financial environment of emerging venture firms.

• Management & Information Systems Review
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• v.38 no.4
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• pp.79-93
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• 2019

This study analyzed the information effect of KOSPI200 market and KOSPI200 futures market and volume synchronized probability of informed trading (VPIN). The data period is 760 days from July 8, 2015 to August 9, 2018, and the intraday trading data is used based on the trading period of the KOSPI 200 Index. The findings of the empirical analysis are as follows. First, as a result of regression analysis of the same parallax, when the level of VPIN is high, the return and volatility of KOSPI200 are high. Second, the KOSPI200 returns before and after the VPIN measurement and the return of the KOSPI200 future had a positive relationship with the VPIN. The cumulative returns of KOSPI200 futures were positive for about 15 minutes.Finally, we find that portfolios with high levels of VPIN showed high KOSPI200 and KOSPI200 futures return. These results confirmed the applicability of VPIN as a trading strategy index. The above results suggest that KOSPI200 and KOSPI200 futures markets will be able to explore volatility and price changes, and also be useful indicators of financial market risk.

• Journal of Distribution Science
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• v.13 no.9
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• pp.37-45
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• 2015

Purpose - This paper's aim is to investigate whether or not gross profitability explains the cross-sectional variation of the stock returns in the Korean stock market. Gross profitability is an alternative profitability measure proposed by Novy-Marx in 2013 to predict cross-sectional variation of stock returns in the US. He shows that the gross profitability adds explanatory power to the Fama-French 3 factor model. Interestingly, gross profitability is negatively correlated with the book-to-market ratio. By confirming the gross profitability premium in the Korean stock market, we may provide some implications regarding the well-known value premium. In addition, our empirical results may provide opportunities for the fund distribution industry to promote brand new styles of funds. Research design, data, and methodology - For our empirical analysis, we collect monthly market prices of all the companies listed on the Korea Composite Stock Price Index (KOSPI) of the Korea Exchanges (KRX). Our sample period covers July1994 to December2014. The data from the company financial statementsare provided by the financial information company WISEfn. First, using Fama-Macbeth cross-sectional regression, we investigate the relation between gross profitability and stock return performance. For robustness in analyzing the performance of the gross profitability strategy, we consider value weighted portfolio returns as well as equally weighted portfolio returns. Next, using Fama-French 3 factor models, we examine whether or not the gross profitability strategy generates excess returns when firmsize and the book-to-market ratio are controlled. Finally, we analyze the effect of firm size and the book-to-market ratio on the gross profitability strategy. Results - First, through the Fama-MacBeth cross-sectional regression, we show that gross profitability has almost the same explanatory power as the book-to-market ratio in explaining the cross-sectional variation of the Korean stock market. Second, we find evidence that gross profitability is a statistically significant variable for explaining cross-sectional stock returns when the size and the value effect are controlled. Third, we show that gross profitability, which is positively correlated with stock returns and firm size, is negatively correlated with the book-to-market ratio. From the perspective of portfolio management, our results imply that since the gross profitability strategy is a distinctive growth strategy, value strategies can be improved by hedging with the gross profitability strategy. Conclusions - Our empirical results confirm the existence of a gross profitability premium in the Korean stock market. From the perspective of the fund distribution industry, the gross profitability portfolio is worthy of attention. Since the value strategy portfolio returns are negatively correlated with the gross profitability strategy portfolio returns, by mixing both portfolios, investors could be better off without additional risk. However, the profitable firms are dissimilar from the value firms (high book-to-market ratio firms); therefore, an alternative factor model including gross profitability may help us understand the economic implications of the well-known anomalies such as value premium, momentum, and low volatility. We reserve these topics for future research.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.4
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• pp.19-38
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• 2019

This study is a study on the effect of patent management on the performance of new business development, focusing on fusion expert collaboration. In the past, most studies on patent management have been influenced by the quantitative patent index on the business performance. Therefore, research on the effect of patent management on the performance of new business development through the cooperation of fusion experts was very insufficient. Therefore, this study examined the influence of existing patent management on the performance of new business development and the causal relationship between the influence of patent management on new business development performance, focusing on fusion expert collaboration. The results of the hypothesis empirical analysis are as follows. First, patent management showed positive (+) influence on convergence expert cooperation. Patents management has a positive effect on fostering convergence specialists and utilizing convergence experts. Second, patent management has a positive effect on new business development performance. Patent management has a positive effect on the success of the business, the achievement of target sales, the development of new markets, the development of new technologies, and the degree of reflection of customer requirements. Third, patent management mediated by convergence expert cooperation has a negative effect on financial aptitude among new business development outcomes. The results of this study are as follows. First, it is concluded that patent management through mediation of convergence expert cooperation has a positive effect on non - financial performance of new business development performance. Financial performance includes business success and achievement of target sales. Non-financial performance includes new technology development and new market development. Therefore, in order to continuously generate business performance of domestic convergence new business development companies, it suggests that we should make efforts to be linked with new business development performance through revitalization of patent management centered on convergence expert cooperation that has positive (+) influence.

• Korean Journal of Construction Engineering and Management
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• v.14 no.3
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• pp.78-87
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• 2013

The modern society has been drastically changed from the industrial economic society to the knowledge based society, to catch up with the knowledge and the change of technology required for the modern people, the people can not live in the modern society without the continued study or education. In case of architectural design firm, it is concentrating on the productivity of enterprise by cultivating the working level through the self education focused on the improvement of inner capacity. In connection with this, the efficiency of enterprises are analyzed by carrying out the Data Envelopment Analysis(DEA) utilizing the financial ratio index in the various field of industries recently, the analysis study for the efficiency utilizing DEA is increased in the construction industries as well. However, in case of construction industries, the study focused on the efficiency of administration only has been progressed, it is the real situation that the approach for the analysis of education efficiency of each enterprise is very insufficient. Therefore, this study analyzed the education efficiency of architectural design firm after the selection of input and output variables by utilizing the DEA model and utilizing the AHP analysis technique by deducting the variables through the preceding study in relation to the education efficiency and the interview with the specialists.

• Journal of Navigation and Port Research
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• v.39 no.3
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• pp.179-184
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• 2015

The purpose of this study is to provide appropriate methodology to ensure the safety and integrity of the production riser in offshore structure. In order to select integrity estimation methodology for production riser, level I and II Non-destructive Damage Evaluation (NDE) methods that were applied to existing structures are classified and reviewed. Numerical analysis is performed to verify the applicability and capability on damage detection of reviewed methods. As a result, the damage detection methodology using modal strain energy is more sensitive in detection of the damage than other methods. In practice, the number of sensors is limited due to the environmental and financial conditions. The impact on damage detection performance by reducing the number of sensors is systematically investigated through a series of numerical analyses and the results are discussed. The optimal number of sensor for the integrity estimation of production riser is recommended.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.48 no.3
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• pp.135-138
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• 2003

Appropriate allocation of personal and financial resources of a research organization is important for the successful accomplishment of its goals. Direct-seeding of rice is a technology compatible with sustainable agriculturer and much research on the technology has been carried out in the research institutes of Rural Development Adminstration (RDA). We analyzed, with a special interest in research on rice direct-seeding technology, patterns of resource allocation in RDA by technology to evaluate congruency levels in research resource allocation. Research on direct-seeding technology had been focused on its fundamental field in the past. However, research to solve the practical difficulties encountered by farmers such as those in seedling establishment weed control, and water management practices, has been increased in recent years. Research resource allocation had largely been made to the projects for variety and seeding-technology development in the early years, however, allocation to the projects for the fertilization, weed control, and water management fields has been increased in recent years. Allocation of resources to the projects in soil management and seedling establishment categories was decreased, indicating that difficulties encountered by farmers in these fields were mostly solved. High congruency between economic outcome of research and allocation of resources by technology categories indicates a rational allocation of resources for research on direct-seeding of vice in RDA.